Fuzzy-neural-network-based quality prediction system for sintering process

Based on the property of a sintering process, a hybrid fuzzy neural networks (FNN) and genetic algorithm (GA) system is proposed to solve the difficult and challenging problem of constructing a system model from the given input and output data to predict the quality of chemical components of the finished sinter mineral. A dual input fuzzy neural networks (DIFNN) system is proposed to represent the fuzzy model and realize fuzzy inference. The learning process of DIFNN is divided into off-line and online learning. In off-line learning, a GA is used to train the DIFNN and construct a system model based on the training data. During online operation, the algorithm inherited from the principle of backpropagation is used to adjust the network parameters and improve the system precision in each sampling period. The process of constructing a system model is introduced in detail. The results obtained from actual prediction demonstrate the performance and capability of the proposed system